Data sources of genome‐wide mutation profiling
We downloaded the HCC genetic mutation data, transcriptome data, and clinical data from the TCGA database (https://tcga-data.nci.nih.gov/tcga/). To identify the somatic mutations of the patients with HCC in the TCGA database, mutation data were downloaded and visualized using the “maftools” package in R software.
Screening of differentially expression genes
Limma package (version: 3.40.2) of R software was used to study the differential expression of mRNAs. The adjusted P-value was analyzed to correct for false positive results in TCGA or GTEX. “Adjusted P<0.05 and Log (Fold Change) >1 or Log (Fold Change) <-1” were defined as the thresholds for the screening of differential expression of mRNAs.
To further confirm the underlying function of potential targets, the data were analyzed by functional enrichment. Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analysis is a practical resource for analytical study of gene functions and associated high-level genome functional information. Gene Ontology (GO) is a widely-used tool for annotating genes with functions, especially molecular function (MF), biological pathways (BP), and cellular components (CC). To better understand the carcinogenesis of mRNA, ClusterProfiler package (version: 3.18.0) in R was employed to analyze the GO function of potential targets and enrich the KEGG pathway.
Selection of TP53-related prognostic signature
Univariate and multivariate cox regression analysis was performed to build the nomogram. The forest was used to show the P value, HR and 95% CI of each variable through ‘forestplot’ R package. A nomogram was developed based on the results of multivariate Cox proportional hazards analysis to predict the 1,3,5-year overall survival. The nomogram provided a graphical representation of the factors, which can be used to calculate the risk for an individual patient by the points associated with each risk factor through ‘rms’ R package.
Kaplan-Meier analysis of overall survival
Raw counts of RNA-sequencing data (level 3), corresponding clinical information and tumor gene mutation MAF data were downloaded from The Cancer Genome Atlas (TCGA) dataset (https://portal.gdc.cancer.gov/). For Kaplan–Meier curves, p-values and hazard ratio (HR) with 95% confidence interval (CI) were generated by log-rank tests and univariate Cox proportional hazards regression. Analysis of risk score, survival status and heatmap were implemented by R foundation and software packages ggrisk, survival and survminer, timeROC. All analytical methods above and R packages were performed by R foundation for statistical computing (2020) version 4.0.3 and software packages ggplot2. p < 0.05 was considered as statistically significant.
Estimation of immune cell score and percentage abundance of tumor infiltrating immune cells
To make reliable immune infiltration estimations, we utilizes the immunedeconv, an R package which integrates EPIC algorithms. CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, TIGIT and SIGLEC15 were selected to be immune-checkpoint–relevant transcripts and the expression values of these eight genes were extracted. Analysis methods and R package were implemented by R foundation for statistical computing (2020) version 4.0.3 and software packages ggplot2 and pheatmap.
Collection of HCC samples
Surgically resected specimens were obtained in October, 2021 from 5 cases of HCC patients at the Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Shanghai, China). This investigation was approved by the ethics committee of Renji Hospital and follow the guidelines of the declaration of Helsinki. All patients provided written informed consent. The inclusion criteria: a. pathological diagnosis was HCC; b. none had received any prior treatment; c. the maximum diameter of single tumor was more than 2 cm; d. Child-Pugh score A or B. The exclusion criteria: a. Child-Pugh score C; b. exceptional circumstances, such as syphilis and Acquired Immune Deficiency Syndrome.
HCC tissues were paraffin-embedded and paraffin sections (4 µm) were prepared. Before incubating with antibodies, samples were blocked with goat serum (cat. no. Ab7481; Abcam) for 10 min at room temperature.For Ki67 staining, sections were incubated with the primary antibody (cat. no. Ab137077; Abcam; 1:500 dilution) and secondary antibody (cat. no. Ab6721; Abcam; 1:1,000 dilution). Sections were dewaxed in xylene and rehydrated in an ethanol gradient of 100, 95 and 80%, and heat-mediated antigen retrieval of the tissue sections was carried out at a temperature of 96-98˚C before they were allowed to cool. Paraffin-embedded sections (4 µm) were incubated with primary antibodies overnight at 4˚C. The secondary antibody was used to detect the primary for 1 h at room temperature. Confocal laser scanning microscopy was performed using an Olympus Corporation BX51 instrument.
Western blot assay
The liver tissue samples were treated with lysis solution. After homogenizing, the proteins extracted from liver tissues samples were quantified using the Bradford method. These protein samples were then separated using SDS-PAGE under a 100v voltage for 1hrs and transferred onto a PVDF membrane. After the transferred membranes were blocked in 5% skim milk for 1hrs, blots were incubated with primary antibodies (cat. no. Ab137077; Abcam; 1:1000 dilution), diluted with 5% skim milk overnight. After the transferred membranes were washed with TBST for three times, blots were re-incubated for 1hrs with second antibodies (cat. no. Ab6721; Abcam; 1:1,000 dilution) diluted with 5% skim milk. Then, sections were washed with TBST for three times. At last, transferred membranes were scanned and analyzed using the image system.
ICB (Immune checkpoint blockage) response prediction
Potential ICB response was predicted with TIDE algorithm(14) and software packages ggplot2(v3.3.3) and ggpubr(0.4.0). All analytical methods above and R packages were performed using R software version v4.0.3 (The R Foundation for Statistical Computing, 2020). p < 0.05 was considered as statistically significant.
All statistical analyses were performed using R version 4.0.3. Two‐tailed Student's t test was used for significance of differences between subgroups. One‐way ANOVA test or Student’s t test were applied to analyse the correlation between risk score and clinicopathological parameters. The data from two groups were compared by Wilcoxon Test and more than three groups by Kruskal-Wallis test. P < 0.05 was considered statistically significant.